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  <div class="section" id="numpy-nanmax">
<h1>numpy.nanmax<a class="headerlink" href="#numpy-nanmax" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="numpy.nanmax">
<code class="sig-prename descclassname">numpy.</code><code class="sig-name descname">nanmax</code><span class="sig-paren">(</span><em class="sig-param">a</em>, <em class="sig-param">axis=None</em>, <em class="sig-param">out=None</em>, <em class="sig-param">keepdims=&lt;no value&gt;</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/numpy/numpy/blob/v1.18.1/numpy/lib/nanfunctions.py#L345-L453"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#numpy.nanmax" title="Permalink to this definition">¶</a></dt>
<dd><p>Return the maximum of an array or maximum along an axis, ignoring any
NaNs.  When all-NaN slices are encountered a <code class="docutils literal notranslate"><span class="pre">RuntimeWarning</span></code> is
raised and NaN is returned for that slice.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>a</strong><span class="classifier">array_like</span></dt><dd><p>Array containing numbers whose maximum is desired. If <em class="xref py py-obj">a</em> is not an
array, a conversion is attempted.</p>
</dd>
<dt><strong>axis</strong><span class="classifier">{int, tuple of int, None}, optional</span></dt><dd><p>Axis or axes along which the maximum is computed. The default is to compute
the maximum of the flattened array.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternate output array in which to place the result.  The default
is <code class="docutils literal notranslate"><span class="pre">None</span></code>; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary. See
<em class="xref py py-obj">ufuncs-output-type</em> for more details.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.8.0.</span></p>
</div>
</dd>
<dt><strong>keepdims</strong><span class="classifier">bool, optional</span></dt><dd><p>If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original <em class="xref py py-obj">a</em>.</p>
<p>If the value is anything but the default, then
<em class="xref py py-obj">keepdims</em> will be passed through to the <a class="reference external" href="https://docs.python.org/dev/library/functions.html#max" title="(in Python v3.9)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">max</span></code></a> method
of sub-classes of <a class="reference internal" href="numpy.ndarray.html#numpy.ndarray" title="numpy.ndarray"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ndarray</span></code></a>.  If the sub-classes methods
does not implement <em class="xref py py-obj">keepdims</em> any exceptions will be raised.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 1.8.0.</span></p>
</div>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>nanmax</strong><span class="classifier">ndarray</span></dt><dd><p>An array with the same shape as <em class="xref py py-obj">a</em>, with the specified axis removed.
If <em class="xref py py-obj">a</em> is a 0-d array, or if axis is None, an ndarray scalar is
returned.  The same dtype as <em class="xref py py-obj">a</em> is returned.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.nanmin.html#numpy.nanmin" title="numpy.nanmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nanmin</span></code></a></dt><dd><p>The minimum value of an array along a given axis, ignoring any NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.amax.html#numpy.amax" title="numpy.amax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">amax</span></code></a></dt><dd><p>The maximum value of an array along a given axis, propagating any NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.fmax.html#numpy.fmax" title="numpy.fmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fmax</span></code></a></dt><dd><p>Element-wise maximum of two arrays, ignoring any NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.maximum.html#numpy.maximum" title="numpy.maximum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">maximum</span></code></a></dt><dd><p>Element-wise maximum of two arrays, propagating any NaNs.</p>
</dd>
<dt><a class="reference internal" href="numpy.isnan.html#numpy.isnan" title="numpy.isnan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isnan</span></code></a></dt><dd><p>Shows which elements are Not a Number (NaN).</p>
</dd>
<dt><a class="reference internal" href="numpy.isfinite.html#numpy.isfinite" title="numpy.isfinite"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isfinite</span></code></a></dt><dd><p>Shows which elements are neither NaN nor infinity.</p>
</dd>
</dl>
<p><a class="reference internal" href="numpy.amin.html#numpy.amin" title="numpy.amin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">amin</span></code></a>, <a class="reference internal" href="numpy.fmin.html#numpy.fmin" title="numpy.fmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fmin</span></code></a>, <a class="reference internal" href="numpy.minimum.html#numpy.minimum" title="numpy.minimum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">minimum</span></code></a></p>
</div>
<p class="rubric">Notes</p>
<p>NumPy uses the IEEE Standard for Binary Floating-Point for Arithmetic
(IEEE 754). This means that Not a Number is not equivalent to infinity.
Positive infinity is treated as a very large number and negative
infinity is treated as a very small (i.e. negative) number.</p>
<p>If the input has a integer type the function is equivalent to np.max.</p>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">3.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="go">array([3.,  2.])</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">array([2.,  3.])</span>
</pre></div>
</div>
<p>When positive infinity and negative infinity are present:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">NINF</span><span class="p">])</span>
<span class="go">2.0</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">])</span>
<span class="go">inf</span>
</pre></div>
</div>
</dd></dl>

</div>


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